Transformers have revolutionized machine learning and artificial intelligence, particularly in developing Large Language Models (LLMs). Their architecture, featuring self-attention mechanisms, allows for efficient processing of sequential data by weighing the significance of words based on context. Comprising an encoder-decoder structure and layer normalization, transformers enhance comprehension and performance across various applications, including Natural Language Processing (NLP), image processing through Vision Transformers, and reinforcement learning. They are foundational to LLMs like GPT-3 and BERT, which excel in understanding and generating human language. As research progresses, transformers promise to drive future innovations in AI technology.